{"id":"https://openalex.org/W4393286888","doi":"https://doi.org/10.1109/wetice57085.2023.10477824","title":"A Federated Learning Architecture for Anomaly Detection on the Edge Using Deep Autoencoders","display_name":"A Federated Learning Architecture for Anomaly Detection on the Edge Using Deep Autoencoders","publication_year":2023,"publication_date":"2023-12-14","ids":{"openalex":"https://openalex.org/W4393286888","doi":"https://doi.org/10.1109/wetice57085.2023.10477824"},"language":"en","primary_location":{"id":"doi:10.1109/wetice57085.2023.10477824","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/wetice57085.2023.10477824","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5081061550","display_name":"David Novoa-Paradela","orcid":"https://orcid.org/0000-0002-9151-3324"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"David Novoa-Paradela","raw_affiliation_strings":["Universidade da Coru&#x00F1;a, CITIC,A Coru&#x00F1;a,Spain"],"affiliations":[{"raw_affiliation_string":"Universidade da Coru&#x00F1;a, CITIC,A Coru&#x00F1;a,Spain","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083105261","display_name":"\u00d3scar Fontenla-Romero","orcid":"https://orcid.org/0000-0003-4203-8720"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Oscar Fontenla-Romero","raw_affiliation_strings":["Universidade da Coru&#x00F1;a, CITIC,A Coru&#x00F1;a,Spain"],"affiliations":[{"raw_affiliation_string":"Universidade da Coru&#x00F1;a, CITIC,A Coru&#x00F1;a,Spain","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046448840","display_name":"Bertha Guijarro\u2010Berdi\u00f1as","orcid":"https://orcid.org/0000-0001-8901-5441"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bertha Guijarro-Berdi\u00f1as","raw_affiliation_strings":["Universidade da Coru&#x00F1;a, CITIC,A Coru&#x00F1;a,Spain"],"affiliations":[{"raw_affiliation_string":"Universidade da Coru&#x00F1;a, CITIC,A Coru&#x00F1;a,Spain","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5094273249","display_name":"Diego Orellana-Ca\u00f1\u00e1s","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Diego Orellana-Ca\u00f1\u00e1s","raw_affiliation_strings":["Universidade da Coru&#x00F1;a, CITIC,A Coru&#x00F1;a,Spain"],"affiliations":[{"raw_affiliation_string":"Universidade da Coru&#x00F1;a, CITIC,A Coru&#x00F1;a,Spain","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5081061550"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21581465,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"194","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11241","display_name":"Advanced Malware Detection Techniques","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/architecture","display_name":"Architecture","score":0.7443274259567261},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7243127822875977},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7122503519058228},{"id":"https://openalex.org/keywords/enhanced-data-rates-for-gsm-evolution","display_name":"Enhanced Data Rates for GSM Evolution","score":0.6167239546775818},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6009888052940369},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.5768027305603027},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5310098528862},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36613547801971436},{"id":"https://openalex.org/keywords/computer-architecture","display_name":"Computer architecture","score":0.33051931858062744},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.06957367062568665}],"concepts":[{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.7443274259567261},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7243127822875977},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7122503519058228},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.6167239546775818},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6009888052940369},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.5768027305603027},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5310098528862},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36613547801971436},{"id":"https://openalex.org/C118524514","wikidata":"https://www.wikidata.org/wiki/Q173212","display_name":"Computer architecture","level":1,"score":0.33051931858062744},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.06957367062568665},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wetice57085.2023.10477824","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/wetice57085.2023.10477824","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1632195884","https://openalex.org/W2122646361","https://openalex.org/W2477849255","https://openalex.org/W2767039516","https://openalex.org/W2903872466","https://openalex.org/W2915771847","https://openalex.org/W2953901595","https://openalex.org/W3011750403","https://openalex.org/W3043320740","https://openalex.org/W3044515030","https://openalex.org/W3086322210","https://openalex.org/W3105324058","https://openalex.org/W3120740533","https://openalex.org/W3134200938","https://openalex.org/W3137762252","https://openalex.org/W3154459044","https://openalex.org/W3193988822","https://openalex.org/W3215850644","https://openalex.org/W4206726406","https://openalex.org/W4383497876"],"related_works":["https://openalex.org/W2806741695","https://openalex.org/W4290647774","https://openalex.org/W3189286258","https://openalex.org/W3207797160","https://openalex.org/W3210364259","https://openalex.org/W4300558037","https://openalex.org/W2912112202","https://openalex.org/W2667207928","https://openalex.org/W4377864969","https://openalex.org/W3030345572"],"abstract_inverted_index":{"Autoencoder":[0],"networks":[1],"are":[2,27,35,150],"widely":[3],"used":[4,48],"in":[5,18,55,102,120,201,206,243,246],"anomaly":[6],"detection,":[7],"however,":[8],"their":[9,16,173,180],"training":[10,174],"can":[11,175,237],"be":[12,205,238],"computationally":[13],"expensive,":[14],"limiting":[15],"use":[17,115],"Edge":[19,121],"Computing":[20,122],"and":[21,90,123,250],"Federated":[22,67,124],"Learning":[23,125],"scenarios,":[24],"where":[25],"devices":[26,155],"usually":[28],"not":[29,71,135,193,258],"very":[30],"powerful.":[31],"In":[32,105,188],"addition,":[33],"there":[34,191],"several":[36,73],"ways":[37],"to":[38,178,184,213],"directly":[39],"or":[40],"indirectly":[41],"attack":[42],"the":[43,46,98,114,117,140,143,147,153,167,185,202,214,244,251,255,260,263],"privacy":[44,99,261],"of":[45,58,75,116,142,208,248,262],"data":[47],"by":[49,223,240],"these":[50],"networks,":[51,63],"which":[52,236],"is":[53,79,134,192,221],"unacceptable":[54],"this":[56,106,189],"type":[57],"scenario.":[59],"Unlike":[60,127],"traditional":[61],"autoencoder":[62],"Deep":[64],"AutoEncoder":[65],"for":[66,113],"learning":[68,76,131,144,212,219],"(DAEF)":[69],"does":[70,257],"require":[72],"rounds":[74],"since":[77],"it":[78,133],"a":[80,194,224,228],"non-iterative":[81],"method.":[82],"This":[83],"implies":[84],"greater":[85],"speed,":[86],"less":[87],"network":[88,119,164,203,245],"traffic,":[89],"lower":[91],"energy":[92],"consumption,":[93],"as":[94,96],"well":[95],"preventing":[97],"attacks":[100],"common":[101],"iterative":[103],"networks.":[104],"paper,":[107],"we":[108],"present":[109],"an":[110],"architecture":[111],"designed":[112],"DAEF":[118,163],"scenarios.":[126],"other":[128],"collaborative":[129],"machine":[130],"approaches,":[132],"server":[136],"based.":[137],"Consequently,":[138],"all":[139],"stages":[141],"process,":[145],"including":[146],"model":[148,182],"aggregation,":[149],"performed":[151],"on":[152],"edge":[154,158],"(nodes).":[156],"Each":[157],"node":[159,200,226],"trains":[160],"its":[161,210],"local":[162,181,211,265],"asynchronously":[165],"concerning":[166],"rest.":[168],"Nodes":[169],"that":[170],"have":[171],"completed":[172],"voluntarily":[176],"request":[177],"add":[179],"information":[183,252],"global":[186,215],"one.":[187],"way,":[190],"unique":[195],"aggregator":[196],"node,":[197],"but":[198],"each":[199],"will":[204],"charge":[207],"adding":[209],"model.":[216],"The":[217],"federated":[218],"management":[220],"handled":[222],"coordinator":[225],"using":[227],"Message":[229],"Queuing":[230],"Telemetry":[231],"Transport":[232],"(MQTT)":[233],"communication":[234],"protocol,":[235],"assumed":[239],"any":[241],"device":[242],"case":[247],"failures,":[249],"exchanged":[253],"between":[254],"nodes":[256],"compromise":[259],"original":[264],"datasets.":[266]},"counts_by_year":[],"updated_date":"2025-12-25T23:11:45.687758","created_date":"2025-10-10T00:00:00"}
